# DeepSeek-V3.2-Exp vs MiMo-V2-Omni

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 42.9 on the Noometry Index. MiMo-V2-Omni costs 1.7× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-mimo-v2-omni
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 6 categories and MiMo-V2-Omni in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 39.9.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- MiMo-V2-Omni accepts more context: 262K tokens versus 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| Provider | DeepSeek | Xiaomi |
| Noometry Index | 44.9 | 42.9 |
| Rank | 68 | 91 |
| Context | 164K | 262K |
| Input $/M | $0.26 | $0.14 |
| Output $/M | $0.38 | $0.28 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- MiMo-V2-Omni: 42.7 (#87)

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1454 | 1467 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- MiMo-V2-Omni: —

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- MiMo-V2-Omni: 28.6 (#86)

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1433 | 1445 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.28 | — |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- MiMo-V2-Omni: 36.4 (#112)

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1435 | 1432 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- MiMo-V2-Omni: 39.9 (#114)

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1436 | 1449 |
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |

## Multimodal

- DeepSeek-V3.2-Exp: —
- MiMo-V2-Omni: 37.9 (#62)

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- MiMo-V2-Omni: 52.0 (#97)

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1409 | 1404 |
| LMArena Chinese | 1461 | 1469 |
| LMArena French | 1435 | 1448 |
| LMArena German | 1440 | 1398 |
| LMArena Japanese | 1377 | 1318 |
| LMArena Korean | 1372 | 1355 |
| LMArena Russian | 1423 | 1412 |
| LMArena Spanish | 1441 | 1432 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- MiMo-V2-Omni: 75.4 (#62)

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1413 | 1428 |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- MiMo-V2-Omni: 44.1 (#72)

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1428 | 1442 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- MiMo-V2-Omni: 61.6 (#83)

| Benchmark | DeepSeek-V3.2-Exp | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1425 | 1424 |
| LMArena Creative Writing | 1403 | 1393 |
| LMArena Multi-Turn | 1427 | 1446 |
| EQ-Bench Creative Writing | 1515 | — |

## FAQ

### Is DeepSeek-V3.2-Exp better than MiMo-V2-Omni?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 42.9 on the Noometry Index. MiMo-V2-Omni costs 1.7× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.2-Exp or MiMo-V2-Omni?

MiMo-V2-Omni is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.

### Is DeepSeek-V3.2-Exp or MiMo-V2-Omni better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 42.7 in the Noometry coding category.

### Which has the bigger context window?

MiMo-V2-Omni does, with 262K tokens against 164K.

### How many benchmarks do DeepSeek-V3.2-Exp and MiMo-V2-Omni share?

17 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and MiMo-V2-Omni has 18.